Monday, March 12, 2007

Modeling Biomedical Networks

steady state vs. equilibrium
If the rate of change of all variables (concentrations of matters) are constant we get a steady state. If Additionally all reactions fluxes are zero, we have an equilibrium.

Calculating steady state
There are several numerical methods to calculate steady state, such as improved Newton method, forward integration and backward integration. However none of them are perfect even to find a steady state in complex systems, which may have several steady states.

Metabolic Control Analysis
MCA describes how the systems reacts to changes of parameters. Elasticities describes how the reaction rates depend on the metabolite concentrations. Control coefficients describes how the systems behavior depend on the reaction rates


Wednesday, March 07, 2007

Install Matlab R2006b

I decide to reinstall MATLAB R2006b mostly because of a new toolbox SymBiology
SimBiology extends MATLAB with tools for modeling, simulating, and analyzing biochemical pathways. You can create your own block diagram model using predefined blocks. You can manually enter in species, parameters, reactions, rules, kinetic laws, and units, or read in Systems Biology Mark-Up Language (SBML) models. SimBiology lets you simulate a model using stochastic or deterministic solvers and analyze your pathway with tools such as parameter estimation and sensitivity analysis.
First get the following MATLAB ISO images at ftp://pxe/software/Matlab2006b (perhaps only available for LAN of USTC)

[Mathworks.Matlab].Mathworks.Matlab.R2006b.UNIX.ISO-TBE-CD1.iso [Mathworks.Matlab].Mathworks.Matlab.R2006b.UNIX.ISO-TBE-CD2.iso [Mathworks.Matlab].Mathworks.Matlab.R2006b.UNIX.ISO-TBE-CD3.iso [Mathworks.Matlab].Mathworks.Matlab.R2006b.UNIX.ISO-TBE.nfo

mount these images and enter the directory where you want to install matlab, create a matlab directory ($MATLAB).

Copy the license file from the first CD. There two license files in CD1/crack license_locked.dat license_server.dat. I copy license_locked.dat to $MATLAB and rename it license.dat. Enter $MATLAB
run CD1/install. The graphic interface is easy to complete.

When I finished the normal install and tried to run matlab. It poped a very lengthy error message java.lang.ExceptionInInitializerError at com.mathworks.mde.filebrowser.FileBrowser.( at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
and collapsed thereafter. But if I run matlab -nojvm, it worked normally.

Solution: the java compiler that comes together with MATLAB caused the above error. Replace it with my own version of java (jre1.5.0_06)
cd $MATLAB/sys/java
mv java java-backup
ln -s path_of_your_own_java java
And then MATLAB works now. Bingo!

PS: kkk recommended another standalone software, Copasi, to build and simulate biomedical networks. Have a look at it.

COPASI is a software application for simulation and analysis of biochemical networks. COPASI — a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.

Current Features:
  • Stochastic and deterministic time course simulation
  • Steady state analysis (including stability)
  • Metabolic control analysis / sensitivity analysis
  • Elementary mode analysis
  • Mass conservation analysis
  • Calculation of Lyapunov exponents
  • Parameter scans
  • Optimization of arbitrary objective functions
  • Parameter estimation using data from time course and/or steady state experiments
  • Sliders for interactive parameter changes
  • Global parameter to change multiple kinetic rates at once
  • Imports and exports SBML (export only in level 2 version 1, import all levels)
  • Loads Gepasi files
  • Export in Berkeley Madonna format and C source code of the ODE system generated from the chemical reactions
  • Versions for MS Windows, Linux, OS X, and Solaris SPARC
  • Command line version for batch processing
  • Visit this page often, new releases will contain many more features!

Still No Sense of Signaling Network Research

As the time of graduation is approaching, I still have no a clear sense of my research subject-insulin signaling network. I would like to admit my laziness and it is mostly because it is a very new and unclear research area. If I have also started with a traditional research, cell culture, gene cloning and purification of proteins, I would mostly finish my research. And now it is too late to switch to an easy topic and it is stupid to do that. Thank that I have read many enlightening papers in this area and learn to use some softwares, why should I give up. It won't be very difficult to graduate no matter what research you have did. It is just a try.

After I realized the above idea, I decided to read systematically publications in this area. Today I am reading the Science STKE Signaling Breakthroughs of the Year. And now another list of paper to be read (The number of papers in this list is increasing expotentially, I don't know when can I have my sense of them)

[1]G. Altan-Bonnet, R. N. Germain, Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 3, e356 (2005).[CrossRef][Medline]

[2]J. R. Pomerening, S. Y. Kim, J. E. Ferrell, Jr., Systems-level dissection of the cell-cycle oscillator: Bypassing positive feedback produces damped oscillations. Cell 122, 565–578 (2005).[CrossRef][Medline]

[3]O. Brandman, J. E. Ferrell, Jr., R. Li, T. Meyer, Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496–498 (2005).[Abstract/Free Full Text]

Friday, March 02, 2007

Paper Analysis -2007-03-02

Reconstruction of Cellular Signaling Networks and Analysis of Their Properties Nature Reviews Molecular Cell Biology 6, 99-111 (2005); doi:10.1038/nrm1570
A NETWORK RECONSTRUCTION includes a chemically accurate representation of all of the biochemical events that are occurring within a defined signalling network, and incorporates the interconnectivity and functional relationships that are inferred from experimental data.
This article give a enlightening theoretical analysis of signal transduction networks: the order of magnitude of numbers of network components (receptor, kinase, phophatase), the order of magnitude of interconnectivity(~2.5 degree of interconnectivity per component). We can use Combinatorial Complexity to characterize this idea. The catalog of network components without post-translational modification can be inferred from the results the genome annotation. The spectrom of network components after PTM and protein-protein interaction during varies states of the network is expected to be assayed with future proteomic experimental techniques (though I feel passive with expectation). But what use or what consequences of these large potential spectrum of various network components means?

The following paper it refers may be worth reading.

Papin, J. A. & Palsson, B. O. The JAK–STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys. J. 87, 37–46 (2004).

Resat, H., Wiley, H. S. & Dixon, D. A. Probability-weighted dynamic Monte Carlo method for reaction kinetics simulations. J. Phys. Chem. B 105, 11026–11034 (2001)

Bhalla, U. S. & Iyengar, R. Emergent properties of networks of biological signaling pathways. Science 283, 381–387 (1999).
Describes some of the first large-scale analyses of signalling reactions.

Hoffmann, A., Levchenko, A., Scott, M. L. & Baltimore, D. The IkappaB–NF-kappaB signaling module: temporal control and selective gene activation. Science 298, 1241–1245 (2002).
Shows the powerful integration of mathematical modelling with experimental investigation

Lee, E., Salic, A., Kruger, R., Heinrich, R. & Kirschner, M. W. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. 1, 116–132 (2003).

Prill, R., Iglesias, P.A. and Levchenko, A. Dynamic Properties of Small Regulatory Motifs Contribute to Biological Network Organization. PLoS Biology 3(11): e343 (2005)

Sivakumaran, S., Hariharaputran, S., Mishra, J. & Bhalla, U. S. The database of quantitative cellular signaling: management and analysis of chemical kinetic models of signaling networks. Bioinformatics 19, 408–415 (2003)

Thursday, March 01, 2007

Omics is Just a Startup

When I was listening the report titled Using Genomics to Explore the Microbial World by Prof. James Tiedje this afternoon, an idea had been daunting in my mind all the time. "Omics is dead" -I forgot where I read this remarks, but it stroke me then and now. Omics is like listing all the components of a computer. However, due to technique limitations and time constraints, we will never be able to get a full list of genes and proteins, though genomics and proteomics optimisticly promised. Even if we could get the full catalogue of human machine, we still can not understand how human body functions and malfunctions, as knowing all the components of a computer does not necessarily imply understanding its working.

Now besides proteomics and genomics, here comes the metabolomics, with similar promising declarations. As the lates Nature essay (Meet the human metabolome)states,
Metabolomics is the study of the raw materials and products of the body's biochemical reactions, molecules that are smaller than most proteins, DNA and other macromolecules. The aim is to be able to take urine, blood or some other body fluid, scan it in a machine and find a profile of tens or hundreds of chemicals that can predict whether an individual is on the road to a disease, say, or likely to experience side-effects from a particular drug.
In fact, researchers in metabolomics are even more optimistic, declaring that
Small changes in the activity of a gene or protein (which may have an unknown impact on the workings of a cell) often create a much larger change in metabolite levels particular concentrations and combinations can reveal something about drugs or disease
However, I am suspecious about their promise. First, considering the great diversity of metabolites in human fluids, we still have not a powerful enough assay to identify the all metabolite in a high-throughout manner and measure their concentrations. Second, the changes in the metabolome is more susceptible to enviromental factors, thus it will be difficult to tell significant changes related to human diseases from temporal fluctuations.

Anyway, let be a little optimistic, omics is just a startup!